Richard Ellem

In many radar or sonar tracking systems, where the state of interest typically includes target position and velocity components, target Doppler measurements may be available in addition to the target position measurements. Using measurements of Doppler information directly in target trajectory estimation leads to a nonlinear filter implementation. In this paper we investigate the methods of using Doppler measurements to improve tracking performance while maintaining the tracker implementation in the linear domain. Two new methods, namely, the one point track initialisation and the Doppler data association are proposed. By applying these two methods on the linear multi-target integrated probabilistic data association algorithm (LMIPDA), we improve tracking performance in terms of reduced number of false tracks and computational load. The merits of the proposed approaches are demonstrated via an active sonar underwater multi-target tracking scenario.

In this paper, a one-point track initiation method is derived for conventional target tracking systems where noisy sensor measurements of both target position and Doppler are available. Tentative tracks are initiated using position measurements from every single scan and the associated Doppler measurements are incorporated to reduce the velocity uncertainties of these initiated track states. The proposed track initiation method is capable of reducing the computational requirements of the system quadratically compared to the classical two-point method when false measurements are present. We further demonstrate that a tracking system which incorporates Doppler measurements both for track initiation and measurement-to-track association can greatly reduce the number of false tracks that are generated and provides greater flexibility for confirming true tracks without sacrificing the system's performance in terms of the number of confirmed false tracks. As a consequence, a tracking system that incorporates the proposed method will exhibit a much shorter true track confirmation delay than a similar system based on the standard approach.

The active sonar environment presents a challenge for any tracking system. Multipath propagation and the fact that targets are, in general, extended objects mean that multiple measurements may be obtained for each target in the surveillance region. In addition, other objects and random scatterers introduce clutter measurements. Whilst pre-processing such as clustering and classification of detections can assist in significantly reducing the number of measurements, there may still be multiple measurements resulting from a single target. This also applies in a multitarget environment, where multiple measurements may be received from each target. Automatic target tracking, which uses measurements in consecutive scans to initiate tracks, generate a large number of false tracks. The key problem for a tracker becomes that of false track discrimination, i.e. confirming true tracks and terminating false tracks. This paper investigates the performance of three trackers for multitarget tracking in the active sonar environment, each using a different data association paradigm. These are the global nearest neighbor (GNN) data association, linear multitarget integrated probabilistic data association (LMIPDA) and nearest neighbour joint probabilistic data association (NNJPDA). A simulation study of these algorithms in a simulated active sonar environment is presented.